





'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCAmelCase									=   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def   __A    (				a_    :	Dict   ,a_    :	Tuple   ,a_    :	List[Any]   ,a_    :	List[str]   ,a_    :	int						):
		for attribute in key.split("."						):
				lowerCAmelCase  :     Optional[int]       						= getattr(a_   ,a_						)

		if weight_type is not None:
				lowerCAmelCase  :     int       						= getattr(a_   ,a_						).shape
		else:
				lowerCAmelCase  :     Union[str, Any]       						= hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''						)

		if weight_type == "weight":
				lowerCAmelCase  :     List[Any]       						= value
		elif weight_type == "weight_g":
				lowerCAmelCase  :     str       						= value
		elif weight_type == "weight_v":
				lowerCAmelCase  :     Optional[int]       						= value
		elif weight_type == "bias":
				lowerCAmelCase  :     Union[str, Any]       						= value
		elif weight_type == "running_mean":
				lowerCAmelCase  :     str       						= value
		elif weight_type == "running_var":
				lowerCAmelCase  :     Optional[Any]       						= value
		elif weight_type == "num_batches_tracked":
				lowerCAmelCase  :     Optional[int]       						= value
		elif weight_type == "inv_freq":
				lowerCAmelCase  :     List[str]       						= value
		else:
				lowerCAmelCase  :     List[str]       						= value

		logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)






def   __A    (				a_    :	int   ,a_    :	Union[str, Any]   ,a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Optional[Any]       						= []
		lowerCAmelCase  :     Any       						= fairseq_model.state_dict()

		lowerCAmelCase  :     Optional[Any]       						= hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				lowerCAmelCase  :     Optional[int]       						= False
				if "conv_layers" in name:
						load_conv_layer(
						    a_   ,a_   ,a_   ,a_   ,hf_model.config.feat_extract_norm == "group"   ,)
						lowerCAmelCase  :     List[str]       						= True
				else:
						for key, mapped_key in MAPPING.items():
								lowerCAmelCase  :     List[str]       						= "wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
										lowerCAmelCase  :     Union[str, Any]       						= True
										if "*" in mapped_key:
												lowerCAmelCase  :     Optional[Any]       						= name.split(a_						)[0].split("."						)[-2]
												lowerCAmelCase  :     int       						= mapped_key.replace("*"   ,a_						)
										if "pos_bias_u" in name:
												lowerCAmelCase  :     Union[str, Any]       						= None
										elif "pos_bias_v" in name:
												lowerCAmelCase  :     str       						= None
										elif "weight_g" in name:
												lowerCAmelCase  :     Optional[int]       						= "weight_g"
										elif "weight_v" in name:
												lowerCAmelCase  :     Tuple       						= "weight_v"
										elif "bias" in name:
												lowerCAmelCase  :     int       						= "bias"
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCAmelCase  :     Dict       						= "weight"
										elif "running_mean" in name:
												lowerCAmelCase  :     List[str]       						= "running_mean"
										elif "inv_freq" in name:
												lowerCAmelCase  :     Any       						= "inv_freq"
										elif "running_var" in name:
												lowerCAmelCase  :     Union[str, Any]       						= "running_var"
										elif "num_batches_tracked" in name:
												lowerCAmelCase  :     str       						= "num_batches_tracked"
										else:
												lowerCAmelCase  :     Optional[Any]       						= None
										set_recursively(a_   ,a_   ,a_   ,a_   ,a_						)
								continue
				if not is_used:
						unused_weights.append(a_						)

		logger.warning(f'''Unused weights: {unused_weights}'''						)






def   __A    (				a_    :	Tuple   ,a_    :	Optional[Any]   ,a_    :	Optional[Any]   ,a_    :	List[Any]   ,a_    :	Tuple						):
		lowerCAmelCase  :     Optional[int]       						= full_name.split("conv_layers."						)[-1]
		lowerCAmelCase  :     Optional[int]       						= name.split("."						)
		lowerCAmelCase  :     Any       						= int(items[0]						)
		lowerCAmelCase  :     List[Any]       						= int(items[1]						)

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
						lowerCAmelCase  :     Any       						= value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
						lowerCAmelCase  :     Tuple       						= value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
						lowerCAmelCase  :     List[str]       						= value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
						lowerCAmelCase  :     List[Any]       						= value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
		else:
				unused_weights.append(a_						)






@torch.no_grad()
def   __A    (				a_    :	int   ,a_    :	Dict   ,a_    :	Optional[Any]=None   ,a_    :	Optional[Any]=None   ,a_    :	List[Any]=True						):
		if config_path is not None:
				lowerCAmelCase  :     str       						= WavaVecaConformerConfig.from_pretrained(a_   ,hidden_act="swish"						)
		else:
				lowerCAmelCase  :     Union[str, Any]       						= WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				lowerCAmelCase  :     Any       						= "rotary"

		if is_finetuned:
				if dict_path:
						lowerCAmelCase  :     Dict       						= Dictionary.load(a_						)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowerCAmelCase  :     List[str]       						= target_dict.pad_index
						lowerCAmelCase  :     Tuple       						= target_dict.bos_index
						lowerCAmelCase  :     str       						= target_dict.eos_index
						lowerCAmelCase  :     Optional[Any]       						= len(target_dict.symbols						)
						lowerCAmelCase  :     Union[str, Any]       						= os.path.join(a_   ,"vocab.json"						)
						if not os.path.isdir(a_						):
								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a_						)						)
								return
						os.makedirs(a_   ,exist_ok=a_						)
						lowerCAmelCase  :     Dict       						= target_dict.indices

						# fairseq has the <pad> and <s> switched
						lowerCAmelCase  :     Dict       						= 0
						lowerCAmelCase  :     List[Any]       						= 1
						with open(a_   ,"w"   ,encoding="utf-8"						) as vocab_handle:
								json.dump(a_   ,a_						)
						lowerCAmelCase  :     Optional[int]       						= WavaVecaCTCTokenizer(
						    a_   ,unk_token=target_dict.unk_word   ,pad_token=target_dict.pad_word   ,bos_token=target_dict.bos_word   ,eos_token=target_dict.eos_word   ,word_delimiter_token="|"   ,do_lower_case=a_   ,)
						lowerCAmelCase  :     Optional[Any]       						= True if config.feat_extract_norm == "layer" else False
						lowerCAmelCase  :     Optional[int]       						= WavaVecaFeatureExtractor(
						    feature_size=1   ,sampling_rate=1_6_0_0_0   ,padding_value=0   ,do_normalize=a_   ,return_attention_mask=a_   ,)
						lowerCAmelCase  :     Optional[Any]       						= WavaVecaProcessor(feature_extractor=a_   ,tokenizer=a_						)
						processor.save_pretrained(a_						)

				lowerCAmelCase  :     List[Any]       						= WavaVecaConformerForCTC(a_						)
		else:
				lowerCAmelCase  :     Any       						= WavaVecaConformerForPreTraining(a_						)

		if is_finetuned:
				lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]   ,arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						)}						)
		else:
				lowerCAmelCase  :     int       						= argparse.Namespace(task="audio_pretraining"						)
				lowerCAmelCase  :     List[Any]       						= fairseq.tasks.setup_task(a_						)

				lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,task=a_						)

		lowerCAmelCase  :     Union[str, Any]       						= model[0].eval()

		recursively_load_weights(a_   ,a_   ,not is_finetuned						)

		hf_wavavec.save_pretrained(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



